Abstract
Data citation provides a valuable method for rewarding citizen scientists by formally acknowledging the contributions that they make to valuable scientific datasets. The difficulty is that citizen science databases that comprise volunteer-generated observations are highly dynamic and contain data contributed by a very large number of volunteers. Moreover, the scientists re-using the citizen science data often only want to cite a small sub-set of the entire database, as it existed at a specific date and time. The majority of data citation approaches assume that the dataset is static, owned by a single agent and the entire dataset is being cited (not just a subset). This paper describes, implements and evaluates an innovative approach to dynamic data citation that potentially overcomes many of the challenges associated with citing sub-sets of constantly changing citizen science datasets and thus enables formal recognition of the volunteers who contributed the data.
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Hunter, J., Hsu, CH. (2015). Formal Acknowledgement of Citizen Scientists’ Contributions via Dynamic Data Citations. In: Allen, R., Hunter, J., Zeng, M. (eds) Digital Libraries: Providing Quality Information. ICADL 2015. Lecture Notes in Computer Science(), vol 9469. Springer, Cham. https://doi.org/10.1007/978-3-319-27974-9_7
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DOI: https://doi.org/10.1007/978-3-319-27974-9_7
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